


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE			: Any				   =					{"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					["""BeitFeatureExtractor"""]
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					["""BeitImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: List[Any]				   =					[
     """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """BeitForImageClassification""",
     """BeitForMaskedImageModeling""",
     """BeitForSemanticSegmentation""",
     """BeitModel""",
     """BeitPreTrainedModel""",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: str				   =					[
     """FlaxBeitForImageClassification""",
     """FlaxBeitForMaskedImageModeling""",
     """FlaxBeitModel""",
     """FlaxBeitPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_beit import BeitFeatureExtractor
  from .image_processing_beit import BeitImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_beit import (
      BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitModel,
      BeitPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_beit import (
      FlaxBeitForImageClassification,
      FlaxBeitForMaskedImageModeling,
      FlaxBeitModel,
      FlaxBeitPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE			: int				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










"""simple docstring"""
def 							A						(				__snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    limit + 1
       __magic_name__      				=    [0] * limit
       for first_term in range(1 ,     __snake_case  ):
              for n in range(__snake_case ,     __snake_case ,     __snake_case  ):
                     __magic_name__      				=    first_term + n / first_term
                     if common_difference % 4:  # d must be divisble by 4
                            continue
                     else:
                            common_difference /= 4
                            if (
                                first_term > common_difference
                                and first_term < 4 * common_difference
                            ):  # since x,y,z are positive integers
                                   frequency[n] += 1  # so z>0 and a>d ,also 4d<a

       __magic_name__      				=    sum(1 for x in frequency[1:limit] if x == 1_0  )

       return count


if __name__ == "__main__":
 print(f"""{solution() = }""")